Identifying tasks in messages

ABSTRACT

Methods and apparatus are described herein for identifying tasks in messages. In various implementations, natural language processing may be performed on a received message to generate an annotated message. The annotated message may be analyzed pursuant to a grammar. A portion of the message may be classified as a user task entry based on the analysis of the annotated message.

BACKGROUND

Users may be inundated with emails texts, voicemails, and/or othermessages asking the users to perform various tasks (e.g., “call Sally at9 am on Tuesday,” “prepare report,” “make reservations at Sal's onSaturday at 8,” etc.). These tasks may be incorporated into messages invarious ways such that users may be required to read messages carefully,and possibly re-read some messages, to ensure they fulfill or otherwisehandle tasks assigned to them. If users do not create to-do lists, itmay be difficult later for users to find tasks assigned to them amongmyriad messages in various formats (e.g., emails, texts, voicemail,etc.).

SUMMARY

The present disclosure is generally directed to methods, apparatus andcomputer-readable media (transitory and non-transitory) for identifyingtasks in messages and performing various responsive actions. In someimplementations, a message may undergo natural language processing togenerate an annotated message that includes various information, such asannotated parts of speech, parsed syntactic relationships, annotatedentity references, clusters of references to the same entity, and soforth. This annotated message may be analyzed, e.g., pursuant to agrammar having one or more rule paths, to determine whether a portion ofthe message qualifies for classification as a task, and if so, what typeof task it should be classified as. In various implementations, asuitable user interface may be identified, and in some cases launched oropened, based on the task type classification, the annotated messageoutput from natural language processing, and other data sources.

In some implementations, a computer implemented method may be providedthat includes the steps of: performing, by a computing system, naturallanguage processing on a received message to generate an annotatedmessage; analyzing, by the computing system, the annotated messagepursuant to a grammar; and classifying, by the computing system, aportion of the message as a user task entry based on the analysis of theannotated message.

This method and other implementations of technology disclosed herein mayeach optionally include one or more of the following features.

In various implementations, the analyzing comprises analyzing, by thecomputing system, the annotated message pursuant to a plurality of rulepaths of the grammar to generate a plurality of candidate user taskentries and associated scores. In various implementations, theclassifying comprises selecting, by the computing system, the user taskentry from the plurality of candidate user task entries based on theassociated scores.

The method may further include identifying, by the computing system, auser interface associated with fulfillment of the user task entry basedon the analysis of the annotated message. In various implementations,performance of the natural language processing comprises identifying, bythe computing system, a reference to a task interaction entity of theuser task entry in the message. In various implementations, performanceof the natural language processing further comprises classifying, by thecomputing system, the first task interaction entity as a person,location or organization. In various implementations, performance of thenatural language processing further comprises identifying, by thecomputing system, a task action of the user task entry.

In various implementations, identifying the user interface comprisesidentifying the user interface based on the task interaction entity andtask action. In various implementations, the method further includesautomatically populating, by the computing system, one or more datapoints associated with the user interface with information based on theanalysis of the annotated message. In various implementations, themethod may further include automatically launching or opening, by thecomputing system, the user interface. In various implementations, thegrammar comprises a context-free grammar.

Other implementations may include a non-transitory computer readablestorage medium storing instructions executable by a processor to performa method such as one or more of the methods described above. Yet anotherimplementation may include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform a method such as one or more of the methods described above.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts described in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example environment in which tasks may beidentified in messages.

FIG. 2 depicts example components of a natural language processingengine.

FIG. 3 schematically demonstrates an example of how a message may beanalyzed using techniques disclosed herein to identify and/or classify atask, and take responsive action.

FIG. 4 depicts a flow chart illustrating an example method ofidentifying tasks in messages.

FIG. 5 schematically depicts an example architecture of a computersystem.

DETAILED DESCRIPTION

FIG. 1 illustrates an example environment in which tasks may beidentified in messages. The example environment includes a client device106 and a user task entry system 102. User task entry system 102 may beimplemented in one or more computers that communicate, for example,through a network (not depicted). User task entry system 102 may be anexample of a system in which the systems, components, and techniquesdescribed herein may be implemented and/or with which systems,components, and techniques described herein may interface. Althoughdescribed as being implemented in large part on a “user task entrysystem” herein, disclosed techniques may actually be performed onsystems that serve various other purposes, such as email systems, textmessaging systems, social networking systems, voice mail systems,productivity systems, enterprise software, search engines, and so forth.

A user may interact with user task entry system 102 via client device106. Other computer devices may communicate with user task entry system102, including but not limited to additional client devices and/or oneor more servers implementing a service for a website that has partneredwith the provider of user task entry system 102. For brevity, however,the examples are described in the context of client device 106.

Client device 106 may be a computer in communication with user taskentry system 102 through a network such as a local area network (LAN) orwide area network (WAN) such as the Internet (one or more such networksindicated generally at 110). Client device 106 may be, for example, adesktop computing device, a laptop computing device, a tablet computingdevice, a mobile phone computing device, a computing device of a vehicleof the user (e.g., an in-vehicle communications system, an in-vehicleentertainment system, an in-vehicle navigation system), or a wearableapparatus of the user that includes a computing device (e.g., a watch ofthe user having a computing device, glasses of the user having acomputing device, a wearable music player). Additional and/oralternative client devices may be provided. Client device 106 mayexecute one or more applications, such as client application 107, thatenable a user to receive and consume messages, create task lists, andperform various actions related to task fulfillment. As used herein, a“message” may refer to an email, a text message (e.g., SMS, MMS), aninstant messenger message, a voicemail, or any other incomingcommunication that is addressed to a user and that is capable ofundergoing natural language processing.

In some implementations, client device 106 and user task entry system102 each include memory for storage of data and software applications, aprocessor for accessing data and executing applications, and componentsthat facilitate communication over network 110. The operations performedby client device 106 and/or user task entry system 102 may bedistributed across multiple computer systems. User task entry system 102may be implemented as, for example, computer programs running on one ormore computers in one or more locations that are coupled to each otherthrough a network.

In various implementations, user task entry system 102 may include anentity engine 120, a user interface engine 122, a natural languageprocessing (NLP) engine 124, a grammar engine 126, and/or a taskclassification engine 128. In some implementations one or more ofengines 120, 122, 124, 126 and/or 128 may be combined and/or omitted. Insome implementations, one or more of engines 120, 122, 124, 126 and/or128 may be implemented in a component that is separate from user taskentry system 102. In some implementations, one or more of engines 120,122, 124, 126 and/or 128, or any operative portion thereof, may beimplemented in a component that is executed by client device 106.

Entity engine 120 may maintain an entity database 125. “Entities” mayinclude but are not limited to people, locations, organizations,actions, objects, and so forth. In various implementations, entitydatabase 125 may include entity data pertinent to a particular userand/or to users globally. For instance, in some implementations, entitydatabase 125 may include a user's contact list, which often may bemaintained as a contact list on the user's smart phone and/or on heremail. In some such cases, entity database 125 may be implementedadditionally or alternatively on client device 106. In someimplementations, entity database may include a global network ofentities that may or may not be pertinent to all users. In variousimplementations, global entity data may be populated over time fromvarious sources of data, such as search engines (e.g., and theirassociated web crawlers), users' collective contact lists, socialnetworking systems, and so forth. In various implementations, entitydata may be stored in entity database 125 in various forms, such as agraph, tree, etc.

User interface engine 122 may maintain an index 127 of user interfaces.As used herein, “user interface” may refer to any visual and/or audiointerface or prompt with which a user may interact. Some user interfacesmay be integral parts of executable software applications, which may beprogrammed using various programming and/or scripting languages, such asC, C#, C++, Pascal, Visual Basic, Perl, and so forth. Other userinterfaces may be in the form of markup language documents, such as webpages (e.g., HTML, XML) or interactive voice applications (e.g., VXML).

In this specification, the term “database” and “index” will be usedbroadly to refer to any collection of data. The data of the databaseand/or the index does not need to be structured in any particular wayand it can be stored on storage devices in one or more geographiclocations. Thus, for example, the indices 125 and/or 127 may includemultiple collections of data, each of which may be organized andaccessed differently.

As described herein, a user task entry (alternatively referred to simplyas a “task”) may include an indication of one or more task actions andan indication of one or more task interaction entities. A task actionmay be an action that a user has interest in completing and/or havingcompleted by one or more other users. For example, a task action may be“buy” and the user may have interest in buying something and/or havinganother person buy something for the user. A task interaction entity isan entity that is associated with the task action. For example, a taskmay have a task action of “buy” and a task interaction entity of“bananas,” and the purpose of the task may be for the user to buybananas.

In some implementations, an indication of the task action and/or thetask interaction entity in a task entry may include an entityidentifier. For example, an indication of the task action “buy” mayinclude an identifier of the entity associated with the action ofbuying. An entity identifier may be associated with an entity in one ormore databases, such as entity database 125. In some implementations, anindication of the task action and/or the task interaction entity in auser task entry may additionally or alternatively include one or moreterms associated with the task action and/or the task interactionentity. For example, an indication of the task action “buy” may includethe terms “buy” and/or “purchase”.

User task entry system 102 may be configured to identify and/or classifyuser task entries within messages based at least in part on analysis ofthe messages pursuant to a grammar. However, designing a grammar capableof facilitating such analysis may be impracticable. Messages may containvirtually any word of any language in any arrangement. Accordingly, NLPengine 124 may be configured to first perform natural languageprocessing on messages to provide what will be referred to herein as an“annotated message.” The annotated message may include various types andlevels of annotations for various aspects of the message. Theseannotations may clarify various aspects of the message, relationshipsbetween terms and sections of the message, and so forth, so thatdesigning a grammar suitable for analyzing messages to identify and/orclassify tasks may become practicable. In various implementations, theannotated message may be organized into one or more data structures,including but not limited to trees, graphs, lists (e.g., linked list),arrays, and so forth.

Annotations that may be provided by NLP engine 124 as part of theannotated message may be best understood with reference to FIG. 2, whichdepicts components of an example NLP engine 124. NLP engine 124 mayinclude a part of speech tagger 230, which may be configured toannotate, or “tag,” words of the message with its grammatical role. Forinstance, part of speech tagger 230 may tag each word with its part ofspeech, such as “noun,” “verb,” “adjective,” “pronoun,” etc.

In some implementations, NLP 124 may also include a dependency parser232. Dependency parser 232 may be configured to determine syntacticrelationships between words of the message. For example, dependencyparser 232 may determine which words modify which others, subjects andverbs of sentences, and so forth. Dependency parser 232 may then makesuitable annotations of such dependencies.

In some implementations, NLP 124 may include a mention chunker 234.Mention chunker 234 may be configured to identify and/or annotatereferences or “mentions” to entities, including task interactionentities, in the message. For example, mention chunker 234 may determineto which person, place, thing, idea, or other entity each noun orpersonal pronoun refers, and may annotate, or “tag,” them accordingly.As another example, mention chunker 234 may be configured to associatereferences to times and/or dates to specific times or dates. Forinstance, assume a message contains the sentence, “Can you pick up somemilk on your way home tonight?” Mention chunker 234 may associate theword “tonight” with today's date, and with a particular time (e.g.,after 5 pm). In some embodiments, mention chunker 234 may determine,e.g., from a user's calendar, when the user is leaving work, and mayassociate that time with the word “tonight.” One or more downstreamcomponents may use this information to create, or help a user create, anappropriate calendar entry and/or to make sure the user receives areminder at an appropriate time (e.g., while driving home).

In some implementations, NLP 124 may include a named entity tagger 236.Named entity tagger 236 may be configured to annotate, or “tag,” entityreferences in the annotated message as a person, location, organization,and so forth. In some implementations, named entity tagger 236 mayidentify one or more task actions of the user task entry. In otherimplementations, one or more other components depicted in FIG. 2 orelsewhere in the figures may be configured to identify one or more taskactions of the user task entry.

In some implementations, NLP 124 may include a coreference resolver 238.Coreference resolver 238 may be configured to group, or “cluster,”references to the same entity based on various contextual cues containedin the message. For example, “Reagan,” “the President,” and “he” in amessage may be grouped together. In some implementations, coreferenceresolver 238 may use data outside of a body or subject of a message,e.g., metadata, to cluster references. For instance, an email or textmay only contain a reference to “you” (e.g., “can you pick up milk onthe way home tonight”). In such case, coreference resolver 238 (oranother component, in different implementations) may resolve thereference to “you” to a person to which the email or text is addressed.

In some implementations, NLP 124 may also include an entity resolver240. Entity resolver 240 may be configured to communicate with entityengine 120 to determine whether entities referenced in the message(e.g., by references tagged by mention chunker 234) are entities thatare contained in entity database 125.

Referring back to FIG. 1, grammar engine 126 may be configured toanalyze the annotated message of NLP engine 124 against a grammar todetermine whether a message includes a user task entry. In someimplementations, grammar engine 126 may analyze the annotated messagepursuant to a plurality of rule paths of the grammar. Each rule path maybe associated with one or more potential types of user task entry. Taskclassification engine 128 may be configured to analyze output of grammarengine 126, and in particular, output of the plurality of rule paths ofthe grammar, to determine a task type. User interface engine 122 may beconfigured to identify a user interface associated with the task, e.g.,based on various data from various other components of FIG. 1.

FIG. 3 depicts one example process flow for identifying tasks in amessage 350. Message 350 may include computer-readable characters and/orsymbols, e.g., of an email, text message, etc. Additionally oralternatively, message 350 may include speech-recognized text (e.g., atranscript) of a voicemail or other audio message. Message 350 may befirst processed by NLP engine 124 to produce the annotated message. Asdepicted in FIG. 3, NLP 124 may obtain data from entity engine 120,e.g., by way of entity resolver 240, to perform various analysis. Theannotated message output by NLP 124 may be provided as input to aplurality of rule paths, 352 a-n (referenced generically by 352), ofgrammar engine 126. Each rule path 352 may define one or more rulesagainst which the annotated message is compared and judged. In someimplementations, the more rules or parameters of a rule path 352 thatare satisfied by the annotated message, the higher a score the annotatedmessage will receive from that rule path 352.

For example, assume rule path 352 a tests the annotated message for atask action of “confer,” a location (e.g., an address), a date and atime. Assume rule path 352 b also tests the annotated message for a taskaction of “confer,” a date and a time, but tests for a telephone numberinstead of a location. If message 350 includes a task, “Make sure youconfer with Judy (555-1234) on June 2^(nd) at 3 pm about party plans,”first rule path 352 a may produce a score of three (because three of thefour items sought were matched), and second rule path 352 b may producea score of four.

Task classification engine 128 may be configured to receive scores fromthe plurality of grammar rule paths 352 a-n and select the mostsatisfactory score (e.g., highest). For example, in the above example,task classification engine 128 would select a type of task associatedwith rule path 352 b. In some implementations, if no score yielded byany rule path 352 satisfies a particular threshold, task classificationengine 128 may determine that no user task entries are present inmessage 350.

UI engine 122 may be configured to identify a user interface associatedwith fulfillment of the user task entry based on various data. Forinstance, UI engine 122 may be in communication with entity engine 120such that it is able to associate a task interaction entity, e.g.,tagged by entity tagger 236, with a particular task. In someimplementations an association between a user interface and an entitymay be based on presence of one or more attributes of the entity in theuser interface. For example, an association between a user interface andan entity may be based on an importance of one or more aliases of theentity in the user interface. For example, appearance of an alias of anentity in important fields and/or with great frequency in a userinterface may be indicative of association of the entity to the userinterface. Also, for example, an association between a user interfaceand an entity may be based on presence of additional and/or alternativeattributes of an entity such as date of birth, place of birth, height,weight, population, geographic location(s), type of entity (e.g.,person, actor, location, business, university), etc.

Take the example described above regarding the task, “Make sure youconfer with Judy (555-1234) on June 2^(nd) at 3 pm about party plans.”UI engine 122 may identify, and in some cases open or launch, a calendaruser interface. In some implementations, UI engine 122 may populate oneor more data points associated with the user interface. Thus, in thesame example, UI engine 122 may launch a calendar entry with the dateand time already set as described in the task.

UI engine 122 may identify, open and/or initiate other types of userinterfaces for other types of tasks. For example, assume message 350includes a task, “Make dinner reservations at Sal's Bistro on Tuesday.”As described above, the plurality of rule paths 352 a-n may be used bygrammar engine 126 to analyze the annotated message output by NLP engine124. The rule path 352 associated with making restaurant reservationsmay yield the highest score, which may lead to its being selected bytask classification engine 128. Additionally, NLP engine 124 may have,e.g., by way of entity tagger 236 sending a query to entity engine 120,identified Sal's Bistro as an entity and tagged it accordingly in theannotated message. In some implementations, other information aboutSal's Bistro not specified in message 350, such as its address and/ortelephone number, may also be obtained from various sources once Sal'sBistro is tagged as an entity.

Using the above-described information, UI engine 122 may identify anappropriate user interface to assist the user in making a reservation atthe restaurant. Various user interfaces 354 a-m are depicted in FIG. 3as being available for use by the user to make the reservation. A firstinterface 354 a may be an interface to online restaurant reservationapplication. A second interface 354 b may be a URL to a webpage, e.g., awebpage hosted by Sal's Bistro that includes an interactive interface tomake reservations. Another user interface 354 m may be a smart phonetelephone interface, which in some cases may be initiated with Sal'sBistro's telephone number already entered, so that the user only need topress “talk” to initiate a call to Sal's. Of course, other types ofinterfaces are possible. In various implementations, one or more datapoints (e.g., interaction entities such as people or organizations, taskactions, times, dates, locations, etc.) may be extracted from theannotated message and provided to whichever user interface is selected,so that the user need not provide this information manually.

Referring now to FIG. 4, an example method 400 of identifying tasks inmessages is described. For convenience, the operations of the flow chartare described with reference to a system that performs the operations.This system may include various components of various computer systems.For instance, some operations may be performed at the client device 106,while other operations may be performed by one or more components ofuser task entry system 102, such as entity engine 120, user interfaceengine 122, NLP engine 124, grammar engine 126, and/or taskclassification engine 128. Moreover, while operations of method 400 areshown in a particular order, this is not meant to be limiting. One ormore operations may be reordered, omitted or added.

Method 400 may begin (“START”) when a message (e.g., 350) is receivedand/or consumed, e.g., at client device 106 or at user task entry system102 (e.g., in a manner that is readily accessible to client device 106).At block 402, the system may perform natural language processing on themessage to generate the annotated message, including performing theoperations associated with the various components depicted in FIG. 2 anddescribed above.

At block 404, the system may analyze the annotated message pursuant to agrammar. For example, at block 406, the system may analyze the annotatedmessage pursuant to a plurality of rule paths (e.g., 352 a-n of FIG. 3)to generate a plurality of candidate user task entries and associatedscores. As described above, candidates having satisfied more parametersof their respective rule paths than others may have higher scores thanothers. In various implementations, each rule path of the grammar, orthe grammar as a whole, may be various types of grammars, such as acontext-free grammar.

At block 408, the system may classify a portion of the message (e.g., asentence, paragraph, selected words, subject line, etc.) as a user taskentry based on analysis of the annotated message provided at block 402.For example, at block 410, the system may select the candidate user taskentry with the highest associated score.

At block 412, the system may select one or more user interfacesassociated with the selected user task entry. At block 414, the systemmay automatically populate one or more data points (e.g., input fields)associated with the selected user interface. For example, if theinterface is an interactive webpage, client 107 may transmit an HTTPrequest with values to assign to various HTTP server variables. At block416, the system may automatically launch or open the selected userinterface. In some embodiments, the operations of blocks 414 and 416 maybe performed in reverse.

FIG. 5 is a block diagram of an example computer system 510. Computersystem 510 typically includes at least one processor 514 whichcommunicates with a number of peripheral devices via bus subsystem 512.These peripheral devices may include a storage subsystem 524, including,for example, a memory subsystem 525 and a file storage subsystem 526,user interface output devices 520, user interface input devices 522, anda network interface subsystem 516. The input and output devices allowuser interaction with computer system 510. Network interface subsystem516 provides an interface to outside networks and is coupled tocorresponding interface devices in other computer systems.

User interface input devices 522 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a touchscreen incorporated into the display, audio inputdevices such as voice recognition systems, microphones, and/or othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computer system 510 or onto a communication network.

User interface output devices 520 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem may include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem may also provide non-visual display such as via audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computer system 510 to the user or to another machine or computersystem.

Storage subsystem 524 stores programming and data constructs thatprovide the functionality of some or all of the modules describedherein. For example, the storage subsystem 524 may include the logic toperform selected aspects of method 400 and/or to implement one or moreof entity engine 120, user interface engine 122, NLP engine 124, grammarengine 126, and/or task classification engine 128.

These software modules are generally executed by processor 514 alone orin combination with other processors. Memory 525 used in the storagesubsystem can include a number of memories including a main randomaccess memory (RAM) 530 for storage of instructions and data duringprogram execution and a read only memory (ROM) 532 in which fixedinstructions are stored. A file storage subsystem 524 can providepersistent storage for program and data files, and may include a harddisk drive, a floppy disk drive along with associated removable media, aCD-ROM drive, an optical drive, or removable media cartridges. Themodules implementing the functionality of certain implementations may bestored by file storage subsystem 524 in the storage subsystem 524, or inother machines accessible by the processor(s) 514.

Bus subsystem 512 provides a mechanism for letting the variouscomponents and subsystems of computer system 510 communicate with eachother as intended. Although bus subsystem 512 is shown schematically asa single bus, alternative implementations of the bus subsystem may usemultiple busses.

Computer system 510 can be of varying types including a workstation,server, computing cluster, blade server, server farm, or any other dataprocessing system or computing device. Due to the ever-changing natureof computers and networks, the description of computer system 510depicted in FIG. 5 is intended only as a specific example for purposesof illustrating some implementations. Many other configurations ofcomputer system 510 are possible having more or fewer components thanthe computer system depicted in FIG. 5.

In situations in which the systems described herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current geographic location), or to controlwhether and/or how to receive content from the content server that maybe more relevant to the user. Also, certain data may be treated in oneor more ways before it is stored or used, so that personal identifiableinformation is removed. For example, a user's identity may be treated sothat no personal identifiable information can be determined for theuser, or a user's geographic location may be generalized wheregeographic location information is obtained (such as to a city, ZIPcode, or state level), so that a particular geographic location of auser cannot be determined. Thus, the user may have control over howinformation is collected about the user and/or used.

While several implementations have been described and illustratedherein, a variety of other means and/or structures for performing thefunction and/or obtaining the results and/or one or more of theadvantages described herein may be utilized, and each of such variationsand/or modifications is deemed to be within the scope of theimplementations described herein. More generally, all parameters,dimensions, materials, and configurations described herein are meant tobe exemplary and that the actual parameters, dimensions, materials,and/or configurations will depend upon the specific application orapplications for which the teachings is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific implementationsdescribed herein. It is, therefore, to be understood that the foregoingimplementations are presented by way of example only and that, withinthe scope of the appended claims and equivalents thereto,implementations may be practiced otherwise than as specificallydescribed and claimed. Implementations of the present disclosure aredirected to each individual feature, system, article, material, kit,and/or method described herein. In addition, any combination of two ormore such features, systems, articles, materials, kits, and/or methods,if such features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

What is claimed is:
 1. A computer-implemented method, comprising:performing, by a computing system, natural language processing on areceived message sent to a user to generate an annotated message,wherein the message was composed by one or more other individualsdifferent than the user; analyzing, by the computing system, theannotated message pursuant to a grammar; classifying, by the computingsystem, a portion of the received message as a task assigned to the userby the one or more other individuals based on the analysis of theannotated message; selecting by the computing system from a plurality ofdistinct user interfaces associated with a plurality of distinctsoftware applications, based on the analysis of the annotated message, auser interface operable by the user to fulfill the task; andautomatically populating, by the computing system, one or more dataentry fields of the selected user interface that are editable by theuser with information based on the analysis of the annotated message. 2.The computer-implemented method of claim 1, wherein: the analyzingcomprises analyzing, by the computing system, the annotated messagepursuant to a plurality of rule paths of the grammar to generate aplurality of candidate user tasks and associated scores; and theclassifying comprises selecting, by the computing system, the task fromthe plurality of candidate user tasks based on the associated scores. 3.The computer-implemented method of claim 1, wherein performance of thenatural language processing comprises identifying, by the computingsystem, a reference to a task interaction entity of the task in themessage.
 4. The computer-implemented method of claim 3, whereinperformance of the natural language processing further comprisesclassifying, by the computing system, the task interaction entity as aperson, location or organization.
 5. The computer-implemented method ofclaim 4, wherein performance of the natural language processing furthercomprises identifying, by the computing system, a task action of thetask.
 6. The computer-implemented method of claim 5, wherein selectingthe user interface comprises selecting the user interface based on thetask interaction entity and task action.
 7. The computer-implementedmethod of claim 1, further comprising automatically launching oropening, by the computing system, the user interface.
 8. Thecomputer-implemented method of claim 1, further comprising selecting, bythe computing system from a plurality of distinct URLs, based on theanalysis of the annotated message, a URL associated with a network-basedresource that is operable by the user to fulfill the task.
 9. A systemincluding memory and one or more processors operable to executeinstructions stored in the memory, comprising instructions to: performnatural language processing on a voicemail addressed to a user togenerate an annotated message, wherein the voicemail message wascomposed by another individual different than the user; analyze theannotated message pursuant to a grammar; classify a portion of thevoicemail as a task assigned to the user by the another individual basedon the analysis of the annotated message; and select, from a pluralityof distinct user interfaces associated with a plurality of distinctsoftware applications, based on the analysis of the annotated message, auser interface operable by the user to fulfill the task.
 10. The systemof claim 9, wherein the system further comprises instructions to:analyze the annotated message pursuant to a plurality of rule paths ofthe grammar to generate a plurality of candidate user tasks andassociated scores; and select the task from the plurality of candidateuser tasks based on the associated scores.
 11. The system of claim 9,wherein the system further comprises instructions to identify areference to a task interaction entity of the task in the voicemail. 12.The system of claim 11, wherein the system further comprisesinstructions to classify the task interaction entity as a person,location or organization.
 13. The system of claim 12, wherein the systemfurther comprises instructions to identify a task action of the task.14. The system of claim 13, wherein the system further comprisesinstructions to select the user interface based on the task interactionentity and task action.
 15. The system of claim 9, wherein the systemfurther comprises instructions to automatically launch or open theselected user interface and populate one or more data entry fields ofthe selected user interface that are editable by the user withinformation based on the analysis of the annotated message.
 16. Thesystem of claim 9, wherein the system further comprises instructions to:perform natural language processing on an email addressed to a user togenerate another annotated message; analyze the another annotatedmessage pursuant to a grammar; and classify a portion of the email as atask assigned to the user based on the analysis of the another annotatedmessage.
 17. A non-transitory computer-readable medium comprisinginstructions that, in response to execution of the instructions by acomputing system, cause the computing system to perform operationscomprising: performing natural language processing on electroniccorrespondence transmitted from a remote computing device to a computingdevice accessible by a user to generate an annotated message prior tothe user opening the electronic correspondence, wherein the electroniccorrespondence is composed by one or more individuals different than theuser; analyzing the annotated message pursuant to a grammar; classifyinga portion of the electronic correspondence as a task assigned to theuser by the one or more individuals based on the analysis of theannotated message; and selecting, by the computing system from aplurality of distinct user interfaces provided by a plurality ofdistinct software applications, based on the analysis of the annotatedmessage, a user interface operable by the user to fulfill the task. 18.A computer-implemented method, comprising: performing, by a computingsystem, natural language processing on electronic correspondencetransmitted over a communication network to a computing deviceaccessible by a user to generate an annotated message, wherein theelectronic correspondence is composed by one or more individualsdifferent than the user; analyzing, by the computing system, theannotated message pursuant to a plurality of rule paths of a grammar togenerate a plurality of candidate user tasks tentatively assigned to theuser by the one or more individuals and associated scores; andselecting, by the computing system, a task assigned to the user from theplurality of candidate user tasks based on the associated scores;selecting, by the computing system from a plurality of distinct userinterfaces provided by a plurality of distinct software applications,based on the analysis of the annotated message, a user interfaceoperable by the user to fulfill the task assigned to the user, whereinthe plurality of distinct user interfaces includes at least one URLassociated with a network-based resource that is operable by the user tofulfill a task; and automatically populating, by the computing system,one or more data entry fields of the selected user interface that areeditable by the user with information based on the analysis of theannotated message.